SE-IYOLOV3: An Accurate Small Scale Face Detector for Outdoor Security

Zhenrong Deng, Rui Yang, Rushi Lan, Zhenbing Liu and Xiaonan Luo
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Zhenrong Deng: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
Rui Yang: Guangxi Key Laboratory of Images and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China
Rushi Lan: Guangxi Key Laboratory of Images and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China
Zhenbing Liu: Guangxi Key Laboratory of Images and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China
Xiaonan Luo: Guangxi Key Laboratory of Images and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China

Mathematics, 2020, vol. 8, issue 1, 1-12

Abstract: Small scale face detection is a very difficult problem. In order to achieve a higher detection accuracy, we propose a novel method, termed SE-IYOLOV3, for small scale face in this work. In SE-IYOLOV3, we improve the YOLOV3 first, in which the anchorage box with a higher average intersection ratio is obtained by combining niche technology on the basis of the k-means algorithm. An upsampling scale is added to form a face network structure that is suitable for detecting dense small scale faces. The number of prediction boxes is five times more than the YOLOV3 network. To further improve the detection performance, we adopt the SENet structure to enhance the global receptive field of the network. The experimental results on the WIDERFACEdataset show that the IYOLOV3 network embedded in the SENet structure can significantly improve the detection accuracy of dense small scale faces.

Keywords: small scale face; SENet; face detection; SE-IYOLOV3 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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